Machine Learning (ML)

Machine Learning (ML) is a subset of Artificial Intelligence that allows computers to “learn” from data. Ordinarily, in programming, we provide data and the expected output, and the machine does the work. In ML, it's a bit different; its goal is generalization, that is, training a model so it can use data it has never seen before and make a fairly accurate description. For ML to work, we need large collections of data. This data is then prepared/cleaned so the machine can read it - like turning text into binary numbers. Next comes the training, the algorithm analyzes the data in order to find patterns, for example, like in spam detection. The machine “learns” by being trained with a large dataset, and at the end, we can use unknown data to get results. ML can be supervised, unsupervised, and refined. Real-world examples of machine learning are the suggestions you see on platforms like YouTube or Netflix, the facial recognition feature on your phone, and language translation tools. In a nutshell, machines are much faster than humans in recognizing repeating patterns, and ML can be very useful, or very harmful, depending on how it’s used.